Image enhancement or filtering techniques enhance the contrast, boost the image details, and produce more vivid colors, and at the same time removes the effects of blur, noise, and compression artifacts. Examples of such filtering methods include weighted least squares (WLS) [1]}, bilateral filtering [2]}, image sharpening, guided filtering [3]}, BFWLS_AVG [4]} and more. Filtering using (1) RGB and (2) RGB-NIR images are used for several applications in computer vision and computational photography applications, such as to improve the contrast of the haze-degraded color images [5]}; tone mapping and detail enhancement [1]}; denoising in RGB videos [7]} and images [8]}; multi-modal medical image fusion from MRI-CT [9]}; illumination transfer from reference to target images [10]}; feature matching [11]}; object recognition and image classification tasks [12]}; biometrics verification tasks [4]}; and the list goes on. To the best of our knowledge, our work is the first to show that the image enhancement can be beneficial for smoke/non-smoke image classification. We compare against various of these filtering methods discussed above in our experimental section.
